Application management computing devices can accelerate and optimize network traffic communicated between server devices and client devices in order to improve the user experience. Application management computing devices can also perform other functions on network traffic, such as load balancing the network traffic to server devices and/or implementing firewalls or other security functionality for the network, for example.
Network traffic handled by application management computing devices includes an increasing amount of streaming video content of various types including HyperText Transfer Protocol (HTTP) Live Streaming (HLS), Dynamic Adaptive Streaming over HTTP (DASH), or Adaptive Bitrate Streaming (ABS), for example. Video segments for streaming video content are generally retrieved using hierarchically structured playlists, whereby a master playlist file stores network addresses of a secondary playlist file which stores network addresses of the video segments. A master playlist file can include network addresses of a plurality of secondary playlist files, each associated with a different bit rate.
Accordingly, upon receipt of the master playlist file, an application, such as a video player executing on a client device, can decide which bit rate is currently most appropriate, or is likely to result in an optimal experience for the user, and request one of the secondary playlist files using the corresponding network address indicated in the master playlist file. Over time, and depending on network characteristics or observations, the application may request various video segments corresponding to different bit rates from several of the secondary playlist files using the corresponding network addresses of the segments indicated in the secondary playlist files.
Hosts or providers of such streaming video content are often interested in the quality of experience (QOE) for users or viewers of the content so that changes can be made to improve poor QOE. Currently, methods for scoring QOE are relatively ineffective, qualitative, and therefore relatively inaccurate. Additionally, current methods for QOE scoring generally require user interaction, such as through surveys or questionnaires.
While some QOE scoring methods have been developed for traditional progressive video streaming that do not require user interaction, these methods are not effective for certain types of streaming video content. For example, these methods have reduced effectiveness for content of the HLS type because multiple connections may be used by a client device to obtain a plurality of segments for a video. In these types of streaming video content, insufficient data is available on a per-connection basis to generate an effective or accurate QOE score for the video.